Comparison of GENIE and conventional supervised classifiers for multispectral image feature extraction

Author

Harvey, Neal R. ; Theiler, James ; Brumby, Steven P. ; Perkins, Simon ; Szymanski, John J. ; Bloch, Jeffrey J. ; Porter, Reid B. ; Galassi, Mark ; Young, A. Cody

Author_Institution

Los Alamos Nat. Lab., NM, USA

Volume

40

Issue

2

fYear

2002

fDate

2/1/2002 12:00:00 AM

Firstpage

393

Lastpage

404

Abstract

The authors have developed an automated feature detection/classification system, called GENetic Imagery Exploitation (GENIE), which has been designed to generate image processing pipelines for a variety of feature detection/classification tasks. GENIE is a hybrid evolutionary algorithm that addresses the general problem of finding features of interest in multispectral remotely-sensed images. The authors describe their system in detail together with experiments involving comparisons of GENIE with several conventional supervised classification techniques, for a number of classification tasks using multispectral remotely sensed imagery

Keywords

feature extraction; genetic algorithms; geophysical signal processing; geophysical techniques; geophysics computing; image classification; multidimensional signal processing; terrain mapping; GENIE; GENetic Imagery Exploitation; IR; feature extraction; geophysical measurement technique; hybrid evolutionary algorithm; image classification; image processing; infrared; land surface; multispectral remote sensing; supervised classifier; terrain mapping; visible; Computer vision; Feature extraction; Genetic programming; Image generation; Image processing; Image segmentation; Multispectral imaging; Pipelines; Remote sensing; Supervised learning;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.992801

Filename

992801